LSE Methods Summer Programme 2019

London School of Economics and Political Science

Kenneth Benoit (kbenoit@lse.ac.uk), Department of Methodology, LSE
Slava Jankin Mikhaylov (jankin@hertie-school.org), Data Science Lab, Hertie School, Berlin
Jack Blumenau (j.blumenau@ucl.ac.uk), Department of Political Science, UCL
Gökhan Çiflikli (g.ciflikli@lse.ac.uk), Department of Methodology, LSE

This repository contains the class materials for the Research Methods, Data Science, and Mathematics course ME314 Introduction to Data Science and Machine Learning taught in July-August 2019 by Kenneth Benoit, Slava Mikhaylov, Jack Blumenau, and Gokhan Ciflikli.

Day Date Instructor Topic
1 Mo 29 Jul KB Overview and introduction to data science
2 Tu 30 Jul KB The Shape of Data
3 We 31 Aug KB Working with Databases
4 Th 1 Aug KB Linear Regression
5 Mo 5 Aug SM Classification
6 Tue 6 Aug SM Non-linear models and tree-based methods
7 Wed 7 Aug GC Resampling methods, model selection and regularization
8 Th 8 Aug KB Unsupervised learning and dimensional reduction
9 Mo 12 Aug JB Text analysis
10 Tu 13 Aug GC Text classification and scaling
11 We 14 Aug JB Topic modelling
12 Th 15 Aug JB Data from the Web
13 Fr 16 Aug   Final Exam

Overview

Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods.

This course integrates prior training in quantitative methods (statistics) and coding with substantive expertise and introduces the fundamental concepts and techniques of Data Science and Big Data Analytics.

Typical students will be advanced undergraduate and postgraduate students from any field requiring the fundamentals of data science or working with typically large datasets and databases. Practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming are also welcome. Because this course surveys diverse techniques and methods, it makes an ideal foundation for more advanced or more specific training. Our applications are drawn from social, political, economic, legal, and business and marketing fields.

Objectives

This course aims to provide an introduction to the data science approach to the quantitative analysis of data using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. We will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods we cover. We also cover data preparation and processing, including working with structured databases, key-value formatted data (JSON), and unstructured textual data. At the end of this course students will have a sound understanding of the field of data science, the ability to analyse data using some of its main methods, and a solid foundation for more advanced or more specialised study.

The course will be delivered as a series of morning lectures, followed by lab sessions in the afternoon where students will apply the lessons in a series of instructor-guided exercises using data provided as part of the exercises. The course will cover the following topics:

Prerequisites

Students should already be familiar with quantitative methods at an introductory level, up to linear regression analysis. Familiarity with computer programming or database structures is a benefit, but not formally required.

Preparing for the course

While we have also made a cloud server available to you for working with all of the assignments, if you want to use these locally or after the course, we recommend that you download and install R and RStudio on your computer.

Detailed instructions can also be found here for installing the tools you need and working with the lab materials.

If you are not already familiar with R, we strongly encourage you to attempt to become familiar before the start of the course. That way, you will spend much less time become familiar with the tools, and be able to focus more on the methods. The following links provide a basic introduction to R, which you can study at your own pace before the course begins.

We strongly recommend you spend some of July and August before the course working through the following materials:

If you start preparing for the course (with the above materials) using your own laptop, it may be more convenient for you to continue using it during the summer school.

Important Specifics

Computer Software

Computer-based exercises will feature prominently in the course, especially in the lab sessions. The use of all software tools will be explained in the sessions, including how to download and install them. All of the class work will be done using R, using publicly available packages.

Main Texts

The primary texts are:

The following are supplemental texts which you may also find useful:

Instructors

Kenneth Benoit is Professor of Computational Social Science at the Department of Methodology, LSE. With a background in political science, his substantive work focuses on political party competition, political measurement issues, and electoral systems. His research and teaching is primarily in the field of social science statistical applications. His recent work concerns the quantitative analysis of text as data, for which he has developed a package for the R statistical software.

Slava Jankin Mikhaylov is Professor of Public Policy and Data Science at the Hertie School of Governance, Berlin. His current work focuses on embedding artificial intelligence and data science in public service delivery.

Jack Blumenau is a Lecturer in Quantitative Methods at the UCL Department of Political Science, and a Data Science Advisor to YouGov. He was previously an ESRC “Future Research Leader” post-doc in the Methodology Department at the LSE. His research is primarily in the fields of legislative and electoral politics.

Gökhan Çiflikli is a Senior Data Scientist at Attest. He specialises in interpretable machine learning, time-series forecasting, and natural language processing. He was previously a research officer in analytic software development in the Department of Methodology at the LSE.

Assessment

Daily lab exercises

These are not assessed, but will form the practical materials for each day’s labs. See these instructions for how to access and work with each day’s exercise.

See https://lse-me314.github.io/instructions for detailed instructions on obtaining and working with each day’s lab materials.

Mid-term project

The class assignment for Day 5 will count as the mid-term assignment, which will count for 25% of the grade. The mid-term is available here.

Exam

Friday, 16 August. This will be a “take-home” exam. You are welcome to use a lab as a quiet room on Friday to complete the assignment, but you can also do this from home. Instructors will be available throughout the day by email for answering any procedural questions or clarifying anything about the exam that is unclear.

Detailed Course Schedule


1. Overview and introduction to data science

We will use this session to get to know the range of interests and experience students bring to the class, as well as to survey the machine learning approaches to be covered. We will also discuss and demonstrate the R software.

Resources
Required reading

2. The Shape of Data

This week introduces the concept of data “beyond the spreadsheet”, the rectangular format most common in statistical datasets. It covers relational structures and the concept of database normalization. We will also cover ways to restructure data from “wide” to “long” format, within strictly rectangular data structures. Additional topics concerning text encoding, date formats, and sparse matrix formats are also covered.

Resources
Required reading

3. Working with Databases

We will return to database normalization, and how to implement this using good practice in a relational database manager, SQLite. We will cover how to structure data, verify data types, set conditions for data integrity, and perform complex queries to extract data from the database. We will also cover authentication and how to connect to local and remote databases.

Resources
Required reading

4. Linear Regression

Linear regression model and supervised learning.

Resources
Required Reading

5. Classification

Logistic regression, discriminant analysis, Naive Bayes, evaluating model performance.

Resources
Required Reading

6. Non-linear models and tree-based methods

GAMs, local regression, decision trees, random forest, boosting.

Resources

7. Resampling methods, model selection and regularization

Cross-validation, bootstrap, ridge and lasso.

Resources
Required Reading

8. Unsupervised learning and dimensional reduction

Cluster analysis, PCA, correspondence analysis, association rules.

Resources
Required reading

9. Text analysis

Working with text in R, sentiment analysis, dictionary methods.

Resources
Required reading

10. Text classification and scaling

Naive Bayes classifier, Wordscores, Wordfish, and correspondence analysis.

Resources
Required reading

Laver, M., Benoit, K., & Garry, J. (2003). Extracting Policy Positions from Political Texts Using Words as Data. American Political Science Review, 97(2), 311-331. doi:10.1017/S0003055403000698

Slapin, J. B. and Proksch, S. (2008), A Scaling Model for Estimating Time‐Series Party Positions from Texts. American Journal of Political Science, 52: 705-722. doi:10.1111/j.1540-5907.2008.00338.x


11. Topic modelling

Latent Dirichlet Allocation, Correlated Topic Model, Structural Topic Model.

Resources
Required reading

12. Data from the Web

The promises and pitfalls of social media data. The Twitter API. The Facebook API. Web scraping. Ethics.

Resources
To use the rtweet package in R: